Measure differentiation and integration error
Compare tabulated Gaussian curves, finite differences and trapezoidal integration with analytic references.
numpy · pandas · scipy · matplotlib · IPython
MATHEMATICS / 01–06
Mathematics & statistics
6 independent, runnable lessons connecting questions, traceable inputs, transparent methods and interpretation. One notebook download starts each workflow.
Learning path 01 → 02 → 03 → 04 → 05 → 06; every lesson also runs independently.
Download this discipline in both languages6 recipes
Find a physical root with a verifiable bracket
Trace bisection, compare a trusted solver and derive Wien displacement from frozen SI constants.
numpy · pandas · scipy · matplotlib · IPython
Inspect a poorly conditioned least-squares problem
Build a polynomial design matrix, inspect singular values, compare solvers and separate coefficient stability from prediction error.
numpy · pandas · matplotlib · IPython
Quantify Monte Carlo uncertainty
Estimate a circle area with seeded random points, use Wilson intervals and inspect repeated-experiment coverage.
numpy · pandas · scipy · matplotlib · IPython
Bootstrap uncertainty in real measurements
Resample an observed Iris group, compare percentile intervals and standard-error formulas, and state the sampling assumptions.
numpy · pandas · matplotlib · IPython
Run permutation tests and correct the full family
Test predeclared Iris group comparisons, use the Monte Carlo +1 correction and reconcile joint Benjamini–Hochberg adjustment.
numpy · pandas · scipy · matplotlib · IPython
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